Crowdsourced Data Utilization for Flood Risk Management
Summary
The integration of crowdsourced data has transformed flood risk management by supplementing traditional monitoring networks with real-time, high-resolution information from the public. Crowdsourced contributions range from geotagged photographs and social media posts to sensor readings and participatory mapping, enabling rapid detection of inundation extents, water depths and infrastructure impacts. Data fusion techniques combine these heterogeneous streams with remote sensing, hydrodynamic models and digital twins to enhance situational awareness, model calibration and early warning systems. This approach addresses data scarcity in ungauged or resource-poor regions and empowers communities through participatory risk governance. It supports urban planners and emergency responders with near-instantaneous flood inundation maps and validates numerical forecasts, thereby improving decision-making before, during and after flood events. The global significance of these methods is evident in applications from dense urban centres to remote catchments, where citizen-sourced observations have bridged critical information gaps and fostered resilience. Ongoing challenges include ensuring data quality, standardising reporting formats and integrating uncertainty quantification, but advances in machine learning and automated filtering continue to refine the reliability and scalability of crowdsourced flood data.
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Crowdsourced Data Utilization for Flood Risk Management publication trend
The graph below shows the total number of articles in crowdsourced data utilization for flood risk management across all publications each year (not limited to Nature Index journals).
Technical terms
Volunteered Geographic Information (VGI): User-generated geospatial data collected via smartphones, apps or web platforms.
Crowdsourced Data: Information gathered from the public, often in real time, to support scientific or decision-making processes.
Data Fusion: The integration of multiple data sources to produce more consistent, accurate and useful information.
Digital Twin: A virtual representation of physical infrastructure or systems used for simulation and scenario analysis.
Early Warning System (EWS): A framework of sensors, analysis tools and communication channels to alert stakeholders of impending flood risks.
References
- The utility of using Volunteered Geographic Information (VGI) for evaluating pluvial flood models. The Science of The Total Environment (2023).
- How Many Floods Have Occurred in China in the Past Decade? A Perspective From Social Media. Earth's Future (2025).
- Rapid flood inundation mapping using social media, remote sensing and topographic data. Natural Hazards (2017).
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